Developing and Using Dynamic Microsimulation Models for

Public Policy Analysis

Cathal O’Donoghue

Teagasc Rural Economy and Development Programme

My Modelling Background

1990’s

2000’s

SWITCH

LIAM

EUROMOD

PENSIM2

MIDAS x 3 LIAM2

Social

Genome

SMILE

2010’s

Farm Level

Enviro-SMILE

Crisis

Modelling

Other

Brazil

Pakistan

Sri Lanka

Nigeria

Estonia

Lithuania

Cross Border

Workers

Retirement

Choice

Public Choice

A Dynamic Microsimulation Model

Dynamic

t –inter-temporal

Micro

i - micro units

Simulation

B – parameter estimates – applied to other variables in model X

Almost all variables endogenous to model

Can respond to policy P

Incorporates individual heterogeneity ε

f() – functional form of regression model

g() – alignment or calibration method using totals C

CPXfgY itititit ,*

Sources of Complexity

Population

Policy

Time

Behaviour

• All Models are wrong – some are useful (Box)

• Multiple dimensions of complexity – models help to manage complexity

• Move to increase complexity of models

• However simpler may be better

• Complexity More costly, time consuming, harder to interpret

• Longitudinal versus Cross-section

Orcutt 1961

DYNASIM I/II

CORSIM

SVERIGE

POLISIM

DYNACAN

DYNASIM III

1960’s

1970’s

1980’s

1990’s

2000’s

LIFEMOD

LIAM

PENSIM

HARDING

PENSIM2

MIDAS x 3 LIAM2

DYNAMOD

APPSIM

INFORM

USA UK

EU

AUS

SAGEMOD

T-DYMM

Lineage of T-DYMM

LSE

Dynamic Models

Database

Model Framework

Analytical Routines:

RR/METR

Tax-Benefit Routine

Output Routine

Behavioural

Routine

If Behaviour depends

on Tax-Benefit System

Models Built

Li and O'Donoghue, 2012

• Very many models built

• Few survive into medium term

Model Frameworks

Microsimulation Model Framework – Model Engine

Expensive to create – 1 to 2 person years (or more)

Because of cost, more effort spend on computing environment than policy

question

Specific not general, so models die after initial use

LIAM

LIAM Objectives

Construct a dynamic microsimulation model flexible enough to cope with future

demands of my research agenda

Limited data at the time

Later objective

Potentially usable elsewhere

Rationale

Computing and Other Costs have slowed down development of dynamic

microsimulation models over the last 30 years.

Model Development still very expensive

Alternatives:

Reusable Code

Use of other models as templates

LIAM

Requirements

Intra - cohort redistribution of the tax-benefit system.

Demographic Ageing and the Income Distribution

Comparisons of welfare state life course redistribution across countries

Improve behavioural equations

Improved data

Savings processes

Life course labour supply

LIAM

Desirable Features

Ease in adding new data

Ease in new adding behavioural information

Can run on a PC

Flexible and Transparent

Robust to Changes

Speed

Allow user to focus more on behaviour than computing

LIAM

Data Structure

Use relational database?

Data storage event driven

Which, When, Who, What

Cohort versus cross-section

Multi-person processes

Defining and initialising variables

Duration data

LIAM

Generalisation - Modularisation

Modularisation

Variable Order

5 Types of Process Module:

transition matrices,

regressions,

marriage market

transformations

tax-benefit system

Discrete time

Tax-Benefit module

LIAM

Implementations of Framework

Irish Tax-Benefit Dynamic Microsimulation Model

Life-cycle redistribution

Pensions analysis and redistribution

EU15 Indirect Tax Model

Expenditure

Indirect Taxation

MIDAL Models

Be, Ge, IT, Lu

T-DYMM

LIAM

LIAM Book - Methodology

Methodological aspects of dynamic

microsimulation models

The life-cycle income analysis model

(LIAM) computing framework

Simulating histories for dynamic

microsimulation models

Simulating earnings

Simulating migration

Alignment and calibration

LIAM Book - Applications

Intra-personal redistribution over the

life-cycle

Financing higher education

Modelling Expenditure and Indirect

Taxation

Analysing the Impact of the 2007

Irish Pensions Green Paper

What are the Consequences of the

European AWG-projections on the

adequacy of pensions

Introducing Political Economy into

Dynamic Microsimulation Modelling

Progress?

Benchmark

Dynamic Microsimulation Model

A demographic module, modelling leaving home, births, deaths partnership

formation and dissolution, disability, education and broad location.

A labour market module containing participation, hours, unemployment and

labour income

A Tax-Transfer and Wealth module containing capital income and the main

tax and transfer instruments

A marriage matching module

A simple macro-economic model and feedback loops linked with the

microsimulation model via alignment.

Monte Carlo Simulation

•DYNAMSIM I – Orcutt et al. (1976)

• Model built in the 1960’s-1970’s

• Seemingly little progress in field

Constraints and Issues - Hoschka (1986)

Many of the behavioural hypotheses in micro-simulation models are of

insufficient theoretical and/or empirical basis

Dynamic changes in the behaviour of the population are mostly not

regarded by micro modellers

The problems of including more than the primary effects of a policy

programme is still unresolved

Quality and accessibility of the data required by micro models often are

restricted severely.

The development of micro-models frequently needs too much time and its costs

are accordingly high

Running micro models usually requires a lot of computer time

The prediction quality of micro-models has not yet been systematically

evaluated and validated

Large microsimulation models are so complex that they are difficult to

comprehend and control.

Progress

Speed and Sample Size

Hardware

Algorithms – DYNACAN, Scott (2001), O‟Donoghue et al. (2009), LIAM2

Validation

Caldwell and Morrison

Micro-econometrics

Better micro models

Spread of Use

Generic Models

ModGen (Wolfson and Rowe, 1998),

UMDBS (Sauerbier, 2002),

GENESIS (Edwards, 2004)

LIAM (O‟Donoghue, 2011) and

LIAM2

Alignment

Alignment

Constrain model outcomes to hit external control totals

Alignment may be used

To „repair‟ the unfortunate consequences of insufficient estimation data by incorporating additional information in the simulations.

To adjust for poor predictive performance of the micro model or its misspecification. Even with perfect data, relationships between dependent variables and explanatory variables may change considerably in countries where substantial structural changes are taking place.

To produce scenarios based on different assumptions.

To establish links between microsimulation models of the household sector and the macro models.

To reduce Monte Carlo variability though its deterministic calculation (Neufeld, 2000). This is particularly useful for small samples to confine the variability of aggregate statistics.

Neufeld, (2000), SOA, (1997), Bacon, (2009). Baekgaard (2002), O‟Donoghue (2010) Li and O‟Donoghue (forthcoming)

Alignment

Li and O'Donoghue, 2012

Alignment

“Microsimulation models usually fail to simulate known time-series data. By

aligning the model, goodness of fit to an observed time series can be

guaranteed.

Opinions vary as to the admissibility of this procedure. Most microsimulation

modellers accept alignment as an unfortunate, but unavoidable necessity while

other thermodynamic modellers (myself among them) consider it to be an

indefensible fiddle which, to use Popper's celebrated phrase, effectively

"immunises the model against empirical refutation".

the only way microsimulation modellers can predict the future is by persuading

someone who knows more than they do to tell them what's going to happen

thermodynamic models are non-alignment microsimulation models and aligned

microsimulation models are irreconcilable”

- Winder (2000)

• Cannot hope to predict the future well

• Is it worth trying to find this “Holy Grail”?

“Failure” to achieve objectives

Perception of failure of earlier models

However

Expectations to high

Predictive Capacity of Models weak

Added Value

Term Forecasting should not be used

Dynamic Microsimulation Models cannot forecast

Not possible to forecast 2008 crisis in 2006 what hope over 50 years

Don‟t oversell

Recreate realistic expectations

Utilise as part of foresighting rather than forecasting

Rather Alignment is a mechanism for Scenario Analysis

Main advantage is that DMM has plausible cross-sectional and longitudinal

distributions

Better to focus attention on

How different macro-economic environments affect these distributions

Improve functioning of Alignment

How to combine Alignment with Behavioural Response to policy and economic

changes

Very limited research on alignment – mainly ad hoc solutions to modelling

requirements big scientific gaps

Behavioural Response

Behavioural Feedback

Li and O'Donoghue, 2012

Behavioural Response

Behavioural Response to Policy Change

Progress cross-sectional labour supply

Use dynamic microsimulation models to generate budget constraints for use as

an input into life-cycle behavioural choice modelling

Important Areas

Retirement Choice

Life-course decision making

Fertility

Education

Savings

Governmental Budget Constraint

Macro-economic Feedback

Dynamic Models

Currently no macro feedback

“Flexible” Government Budget Constraint

Not really a

As population ages

Pressure on financial sustainability

What about wider macro effects

Useful to consider a link to a macro-economic framework with different “closure”

assumptions

Ageing and Political Preferences

Fixed Government Budget Constraint

How to adjust policies?

Option – incorporate a public preference model

As population ages

Changed pattern of preferences

Transfers to elderly drive fiscal imbalance, but group becomes politically stronger

Abid Fourati and O‟Donoghue (2010)

Collect survey on public preferences to pensions policy Choice experiment

Estimate a choice model based around policy attributes and outcomes for different groups

Simulate policy preferences at citizen level

Scale preferences to social preference challenge in relation to how voting system works in respect to individual policies

Requires multiple run of model

Observe trade-off between personal return, poverty reduction and cost

Under status quo preference for universal pensions but not optimal from poverty perspective

Under population ageing preference shift to a lower cost version with later retirement

Higher incomes prefer earnings related system

Future Directions

Methodological Challenges

Unit of Analysis

Family versus Household

Household Formation and Dissolution dynamics

How to incorporate alignment and behavioural response

Governmental Budget Constraint

Macro-economic constraints

Political constraints

Confidence Intervals

Monte Carlo

Intra-household-Intertemporal-Cross-sectional

Simulation properties

How to generate long term stable employment patterns

Understand earnings dyanmics

Validate historical simulations

Base-Sample Size

Li and O'Donoghue, 2012

Areas of Analysis

Big Issues

Ageing

Climate Change

New Areas

Children

Health

Environment

Short term impacts – Fiscal Crisis

Linking LIAM based models with EUROMOD

EUROMOD

Financed by EU Commission since 1993

Tax-Benefit Systems of EU countries

Focus on Policies to Alleviate Poverty and Social Exclusion

Consistent Comparative Framework

Data

Policy

Comparative Analysis

Necessary in understanding increased fiscal coordination

First National MSM in Austria, Greece, Portugal, Lux.

Challenges

Complexity

GUI helps – but many systems

Linking LIAM models to EUROMOD

Convert LIAM Output into EUROMOD input

Feasible

Requires the same variables

May require additional variables

Italian model Bank of Italy Data

Possible now

Luxembourg model

Integrate LIAM with EUROMOD

Undertaken in LIAM1 – but not available now with simplified EUROMOD code

Indirect Tax

My PhD

Advantage

Can call EUROMOD to generate feedbacks from policy to behaviour

Would require significant work Joint project?

Sustainability and Generating Forward Momentum

Reasons for lack of Progress – Tacit Knowledge

Given that nearly 40 years have passed,

the rate of progress it can be argued has been relatively slow

Knowledge Transfer Mechanism

Tacit knowledge

Codified knowledge

Focus on Tacit Knowledge

Networks

Documentation - aim to facilitate other team members utilising the models

Where knowledge codified

mainly been via books and conference presentations which may have been non-peer reviewed, had limited coverage, often went out of print, may have only been available to those who attended an event and were rarely included in usual citation indices and searchable databases.

Where papers were published in peer reviewed formats, they were typically in journals where the focus was on the application rather than the methodology

•A significant proportion of the methods used in the field are not formally

codified,

• meaning that new models have had to reinvent the wheel and re-develop

existing methods over and over again.

Reasons for lack of Progress – Ownership Model

Proprietary versus open source

Proprietary

Code or coding consultancy has been sold to potential clients

Intellectual property makes sense when an economic return can be gained

and incentives private R&D

Relatively small demand for these tools by clients with the capacity to pay

for them, it seems to be a business model that will stymie intellectual

development

Open source

Collective gains

Private gains via citation and scientific reputation

Peer-review quality control

Emphasis on public good nature of research

Funding mechanisms

Business Models

Large Projects

Pensim2, DYNACAN, APPSIM

Start from scratch

Large resources

PhD Based

LIAM, CORSIM

Incremental construction

Lots of small resources

Network Based

MIDAL, LIAM2

Shared resources

Open source

Sustainability risk lack of codification

However spread risk

European Microsimulation Meeting

May 17-19th Dublin, hosted by Teagasc/IZA/UNICEF/NUIM

Paper submission deadline February 3rd

Includes meeting of European Dynamic Microsimulation Model network

Contact Cathal O‟Donoghue <cathal.odonoghue@teagasc.ie>

European Meeting of the International Microsimulation

Association, Dublin 17-19 2012

Thank You